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https://github.com/tpaviot/processscheduler

A Python package for automatic and optimized resource scheduling
https://github.com/tpaviot/processscheduler

constraint-satisfaction-problem project-management resource-allocation scheduling

Last synced: 1 day ago
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A Python package for automatic and optimized resource scheduling

Host: GitHub
URL: https://github.com/tpaviot/processscheduler
Owner: tpaviot
License: gpl-3.0
Created: 2020-12-21T10:49:42.000Z (about 4 years ago)
Default Branch: master
Last Pushed: 2025-01-16T13:32:48.000Z (17 days ago)
Last Synced: 2025-01-25T15:06:53.533Z (8 days ago)
Topics: constraint-satisfaction-problem, project-management, resource-allocation, scheduling
Language: Jupyter Notebook
Homepage: https://processscheduler.github.io/
Size: 10 MB
Stars: 65
Watchers: 5
Forks: 23
Open Issues: 22
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt

Awesome Lists containing this project

README

        # ProcessScheduler

[![Codacy Badge](https://app.codacy.com/project/badge/Grade/7221205f866145bfa4f18c08bd96e71f)](https://www.codacy.com/gh/tpaviot/ProcessScheduler/dashboard?utm_source=github.com&utm_medium=referral&utm_content=tpaviot/ProcessScheduler&utm_campaign=Badge_Grade)

[![codecov](https://codecov.io/gh/tpaviot/ProcessScheduler/branch/master/graph/badge.svg?token=9HI1FPJUDL)](https://codecov.io/gh/tpaviot/ProcessScheduler)

[![Azure Build Status](https://dev.azure.com/tpaviot/ProcessScheduler/_apis/build/status/tpaviot.ProcessScheduler?branchName=master)](https://dev.azure.com/tpaviot/ProcessScheduler/_build?definitionId=9)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tpaviot/ProcessScheduler/HEAD?filepath=examples-notebooks)

[![PyPI version](https://badge.fury.io/py/ProcessScheduler.svg)](https://badge.fury.io/py/ProcessScheduler)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4480745.svg)](https://doi.org/10.5281/zenodo.4480745)

ProcessScheduler is a Python package for optimizing scheduling problems using advanced constraint satisfaction techniques. It provides an intuitive API for modeling complex scheduling scenarios while handling the underlying mathematical computations transparently.

## Updates

-   2024/01/31: Release 2.0.0

-   2024/01/30: Release 2.0.0a

-   2023/12/13: Huge on-going refactoring [#133](https://github.com/tpaviot/ProcessScheduler/issues/133)

-   2023/12/12: Release 0.9.4

## Key Features

- **Task Management**

  - Define tasks with duration, priority, and work requirements

  - Support for fixed, variable, and zero-duration tasks

  - Optional tasks and task dependencies

- **Resource Handling**

  - Individual workers with productivity and cost parameters

  - Resource pools with shared skills

  - Resource availability and unavailability periods

- **Constraint Modeling**

  - Rich set of task and resource constraints

  - First-order logic operations (NOT, OR, XOR, AND, IMPLIES, IF/THEN/ELSE)

  - Buffer management for material consumption/production

- **Optimization & Analysis**

  - Multi-objective optimization (makespan, flowtime, cost)

  - Custom performance indicators

  - Gantt chart visualization

  - Export to JSON, SMT-LIB 2.0, Excel formats

## Installation

### Basic Installation

```bash

pip install ProcessScheduler==2.0.0

```

### Full Installation (with optional dependencies)

```bash

pip install ProcessScheduler[full]==2.0.0

# Or install optional dependencies separately:

pip install matplotlib plotly kaleido ipywidgets isodate ipympl psutil XlsxWriter

```

## Documentation & Examples

- [Documentation](https://processscheduler.github.io/)

- [Interactive Examples](https://mybinder.org/v2/gh/tpaviot/ProcessScheduler/HEAD?filepath=examples-notebooks) (via Binder)

## Quick Start

```python

import processscheduler as ps

# a simple problem, without horizon (solver will find it)

pb = ps.SchedulingProblem('HelloWorldProcessScheduler')

# add two tasks

task_hello = ps.FixedDurationTask('Process', duration=2)

task_world = ps.FixedDurationTask('Scheduler', duration=2)

# precedence constraint: task_world must be scheduled

# after task_hello

ps.TaskPrecedence(task_hello, task_world)

# solve

solver = ps.SchedulingSolver(pb)

solution = solver.solve()

# display solution, ascii or matplotlib gantt diagram

solution.render_gantt_matplotlib()

```

![png](examples-notebooks/pics/hello_world_gantt.svg)

## Code quality

ProcessScheduler uses the following tools to ensure code quality:

-   unittests,

-   code coverage (coverage.py, codecov.io),

-   continuous-integration at MS azure,

-   static code analysis (codacy),

-   spelling mistakes tracking (codespell),

-   code formatting using the black python formatter

## License/Author

ProcessScheduler is distributed under the terms of the GNU General Public License v3 or (at your option) any later version. It is currently developed and maintained by Thomas Paviot ([email protected]).